Case Studies 2022L XAI-tabular - Homework I

Mikołaj Piórczyński

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Preparing model

Explaining model

Making prediction

Break Down

Based on break down plot, the greatest negative inpact on prediction has lead_time, which equals to number of days between the date of booking and the arrival or cancelation date. Also deposite_type has meaningful effect.

Shapley values

We see that small lead_time decreases probability of cancelation and it seems to be logical because it's easier to plan our immediate future than our plans for next few months. Fact that someone is repeated guest also makes cancelation less likely.

Let's note that on shap plot customer_type has positive attribution to model prediction while on break down it has really small but still negative. It may suggest that there are some interactions between customer_type and other variables.

Different effects

We see that this time lead_time which is much bigger than in previous example has the greatest positive contribution to model prediction. Once again, this seems intuitive because, as I mentioned earlier, a lot can happen in such a long time, e.g. someone can lose their job or a war can break out.